H_∞ Stability of Neural Networks Switched at An Arbitrary Time
Choon Ki Ahn · 2012
This article proposes a novel approach to stability analysis of neural networks switched at an arbitrary time. First, a new condition for H_∞ stability of switched neural networks is proposed. Second, a new H_∞ stability condition in the form of linear matrix inequality (LMI) for these neural networks is proposed. These conditions ensure to reduce the H_∞ norm from the external input to the state vector within a disturbance attenuation level. Without the external input, the proposed conditions also guarantee asymptotic stability.